Meta Framework for AI-to-AI Interaction (MAI²) in Professional Contexts

Abstract: Rapid advances in Artificial Intelligence (AI) have led to autonomous agents that not only respond to humans but also interact directly with other AI agents. They are not just exchanging information but also making decisions, collaborating, and even competing as they transform several business functions. As a result, the emerging field of AI-to-AI interaction poses significant challenges around how agents collaborate and how their decisions impact business outcomes. Most existing AI agents depend on strict, rule-based communication. This approach falls short when context changes dynamically, new situations emerge, or conflicting priorities arise amongst the agents. Our research addresses these critical gaps identified through a systematic review of multi-agent systems, communication models, and interaction design. Building on the insights from our multiple-case study research on HumanAI interaction, we developed the Meta Framework for AI-to-AI Interaction (MAI²). This framework is devised around six interconnected layers that make AI-to-AI interaction reliable and trustworthy. The aspirational layer of the framework establishes the agents’ goals and values, the cognitive layer supports reasoning and real-world perception, and the strategic layer focuses on planning and execution. The governance layer ensures the system remains accountable through oversight. The synchronisation layer ensures that different agents work together smoothly. The interactional layer handles the nuts-and-bolts of communication. These layers, together, outline how AI agents collaborate, coordinate, and remain aligned with human values and expectations. MAI² is designed to enable AI agents to learn from each other, evolve together, and adapt over time to collaborate responsibly and effectively. This paper aims to advance AI-to-AI interaction by providing a structured starting point while acknowledging the limitations of its validation across diverse professional contexts.